Freecenta, Helna (2022) IMPLEMENTASI LONG SHORT TERM MEMORY (LSTM) PADA DATA TIME SERIES UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KAB.MALANG). Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Climate change is a phenomenon that has an impact on life, one of the climate indicators that has an impact is rainfall. This natural phenomenon cannot be separated from the triggering factors, one of which is the El Nino and La Nina phenomena. The impact of this phenomenon causes losses in various sectors such as agriculture, transportation and also traditional industries. affected by rainfall. Along with the development of technology, a prediction system will be made. In this study, the prediction system used is using the Long Short Term Memory (LSTM) algorithm where the data used is rainfall data with locations in the Kab. Poor. The data used in this study is rainfall data from January 2010 to December 25, 2021, where data from January 2010 – December 2020 as training data and January 2021 – 25 December 2021 as testing data. In this study, two test scenarios were used. The first trial scenario with 4 LSTM layers where in each layer there are 100 neurons. The second trial scenario with 2 LSTM layers where in each layer there are 50 neurons. The highest model accuracy values obtained during this study were MAE of 7.90, RMSE of 10.16, and MSE of 103.37.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Helna Freecenta | ||||||||||||
| Date Deposited: | 24 Jan 2022 06:37 | ||||||||||||
| Last Modified: | 24 Jan 2022 06:37 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/4709 |
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